Machine learning based on neural networks (NN) [1]}, [2]} has revealed a remarkable potential in solving a wide variety of complex problems. More recently, a number of first applications to quantum physics have emerged. These include the identification of quantum phases of matter and learning phase transitions [3]}, [4]}, [5]}, [6]}, [7]}, quantum state tomography [8]}, [9]}, quantum error correction and decoding [10]}, [11]}, improving quantum Monte Carlo methods [12]}, solving optimization problems encoded in the ground state of a quantum many-body Hamiltonian [13]}, [14]}, and tackling quantum many-body dynamics [13]}, [16]}, [17]}. Our work provides a step forward in the latter direction.
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